aiqbench analysis
This research demonstrates that the ability to track entities within naturalistic narratives emerges in language models as small as 410 million parameters, significantly lower than previously estimated scales. The study reveals that while human performance degrades with narrative complexity, contemporary AI models not only match but exceed human capabilities in this core aspect of language understanding. These findings suggest that sophisticated discourse comprehension is a fundamental capability that appears early in the scaling trajectory of neural networks rather than requiring massive parameter counts or specialized code training.
Entity tracking emerges in sub-billion parameter language models and exceeds human performance in naturalistic narratives
arXiv:2608.18083v1 Announce Type: new Abstract: Understanding language requires tracking entities across discourse - i.e., knowing where things are and how they change, even when not explicitly stated. Whether language models perform such tracking in a human-like fashion remains unclear, in part because existing evaluations rely on artificial tasks, far removed from natural language comprehension, and lack comparisons to humans. Here, we evaluate entity tracking in both language models and humans (N = 48) using naturalistic narratives at multiple levels of complexity. In humans, we find that entity tracking degrades specifically with narrative complexity, not narrative length. In language models, we find that human-level entity tracking is already present at 410 million parameters - well below the multi-billion parameter, code-specialised models identified by prior work - and improves with scale, with contemporary models far exceeding human performance. Together, these results demonstrate that entity tracking, a core component of language understanding, emerges at model scales far smaller than previously thought.
Abstract:Understanding language requires tracking entities across discourse - i.e., knowing where things are and how they change, even when not explicitly stated. Whether language models perform such tracking in a human-like fashion remains unclear, in part because existing evaluations rely on artificial tasks, far removed from natural language comprehension, and lack comparisons to humans. Here, we evaluate entity tracking in both language models and humans (N = 48) using naturalistic narratives at multiple levels of complexity. In humans, we find that entity tracking degrades specifically with narrative complexity, not narrative length. In language models, we find that human-level entity tracking is already present at 410 million parameters - well below the multi-billion parameter, code-specialised models identified by prior work - and improves with scale, with contemporary models far exceeding human performance. Together, these results demonstrate that entity tracking, a core component of language understanding, emerges at model scales far smaller than previously thought.
Computation and Language (cs.CL)
arXiv:2608.18083 [cs.CL]
(or arXiv:2608.18083v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.18083 Focus to learn more arXiv-issued DOI via DataCite
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